Overview

Dataset statistics

Number of variables25
Number of observations10344
Missing cells16572
Missing cells (%)6.4%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.6 MiB
Average record size in memory160.0 B

Variable types

Numeric11
Text10
Categorical4

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Length is highly overall correlated with Width and 6 other fieldsHigh correlation
Width is highly overall correlated with Length and 6 other fieldsHigh correlation
Depth is highly overall correlated with Length and 3 other fieldsHigh correlation
Number of Cabins is highly overall correlated with Length and 3 other fieldsHigh correlation
Number of beds is highly overall correlated with Length and 2 other fieldsHigh correlation
Fuel Capacity is highly overall correlated with Length and 1 other fieldsHigh correlation
PriceClean is highly overall correlated with Length and 3 other fieldsHigh correlation
Amount_in_USD is highly overall correlated with Length and 4 other fieldsHigh correlation
Type is highly overall correlated with Fuel TypeHigh correlation
Fuel Type is highly overall correlated with TypeHigh correlation
Material is highly imbalanced (55.3%)Imbalance
Fuel Type is highly imbalanced (59.4%)Imbalance
Currency is highly imbalanced (67.0%)Imbalance
Manufacturer has 1390 (13.4%) missing valuesMissing
Material has 1832 (17.7%) missing valuesMissing
Engine has 809 (7.8%) missing valuesMissing
Engine Performance has 2281 (22.1%) missing valuesMissing
Fuel Type has 2322 (22.4%) missing valuesMissing
Number of views last 7 days has 363 (3.5%) missing valuesMissing
Comments has 3266 (31.6%) missing valuesMissing
Equipment has 4170 (40.3%) missing valuesMissing
Depth is highly skewed (γ1 = 25.49881832)Skewed
Displacement is highly skewed (γ1 = 41.40589019)Skewed
Number of Cabins is highly skewed (γ1 = 21.64508509)Skewed
Number of beds is highly skewed (γ1 = 33.63724318)Skewed
Year Built has 567 (5.5%) zerosZeros
Depth has 6746 (65.2%) zerosZeros
Displacement has 5828 (56.3%) zerosZeros
Number of Cabins has 3869 (37.4%) zerosZeros
Number of beds has 3937 (38.1%) zerosZeros
Fuel Capacity has 3530 (34.1%) zerosZeros
Engine Hours has 5211 (50.4%) zerosZeros

Reproduction

Analysis started2023-09-21 20:28:55.609014
Analysis finished2023-09-21 20:29:44.636865
Duration49.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Year Built
Real number (ℝ)

ZEROS 

Distinct122
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1894.9372
Minimum0
Maximum2021
Zeros567
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:44.917776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11996
median2007
Q32017
95-th percentile2020
Maximum2021
Range2021
Interquartile range (IQR)21

Descriptive statistics

Standard deviation456.63217
Coefficient of variation (CV)0.24097483
Kurtosis13.269425
Mean1894.9372
Median Absolute Deviation (MAD)10
Skewness-3.9044256
Sum19601230
Variance208512.93
MonotonicityNot monotonic
2023-09-21T16:29:45.362632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020 1306
 
12.6%
2019 688
 
6.7%
0 567
 
5.5%
2008 478
 
4.6%
2007 411
 
4.0%
2006 407
 
3.9%
2018 361
 
3.5%
2017 326
 
3.2%
2005 325
 
3.1%
2009 315
 
3.0%
Other values (112) 5160
49.9%
ValueCountFrequency (%)
0 567
5.5%
1885 1
 
< 0.1%
1889 2
 
< 0.1%
1895 1
 
< 0.1%
1897 1
 
< 0.1%
1898 1
 
< 0.1%
1900 1
 
< 0.1%
1901 3
 
< 0.1%
1902 1
 
< 0.1%
1903 3
 
< 0.1%
ValueCountFrequency (%)
2021 62
 
0.6%
2020 1306
12.6%
2019 688
6.7%
2018 361
 
3.5%
2017 326
 
3.2%
2016 241
 
2.3%
2015 209
 
2.0%
2014 184
 
1.8%
2013 162
 
1.6%
2012 192
 
1.9%

Length
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.036446
Minimum0
Maximum100
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:45.816492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median10
Q313
95-th percentile22
Maximum100
Range100
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9914314
Coefficient of variation (CV)0.54287687
Kurtosis18.464477
Mean11.036446
Median Absolute Deviation (MAD)3
Skewness2.7274486
Sum114161
Variance35.89725
MonotonicityNot monotonic
2023-09-21T16:29:46.244205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1131
10.9%
7 1076
10.4%
9 939
9.1%
11 856
 
8.3%
8 848
 
8.2%
10 772
 
7.5%
5 706
 
6.8%
13 648
 
6.3%
12 577
 
5.6%
14 504
 
4.9%
Other values (51) 2287
22.1%
ValueCountFrequency (%)
0 10
 
0.1%
1 6
 
0.1%
2 9
 
0.1%
3 36
 
0.3%
4 231
 
2.2%
5 706
6.8%
6 1131
10.9%
7 1076
10.4%
8 848
8.2%
9 939
9.1%
ValueCountFrequency (%)
100 1
< 0.1%
93 1
< 0.1%
86 1
< 0.1%
78 1
< 0.1%
69 1
< 0.1%
67 1
< 0.1%
56 2
< 0.1%
54 2
< 0.1%
53 1
< 0.1%
52 1
< 0.1%

Width
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0243619
Minimum0
Maximum25
Zeros69
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:46.593218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2756073
Coefficient of variation (CV)0.42177733
Kurtosis12.352947
Mean3.0243619
Median Absolute Deviation (MAD)1
Skewness1.6665854
Sum31284
Variance1.627174
MonotonicityNot monotonic
2023-09-21T16:29:46.943165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 3887
37.6%
3 2782
26.9%
4 2231
21.6%
5 686
 
6.6%
1 320
 
3.1%
6 222
 
2.1%
7 93
 
0.9%
0 69
 
0.7%
8 24
 
0.2%
9 16
 
0.2%
Other values (5) 14
 
0.1%
ValueCountFrequency (%)
0 69
 
0.7%
1 320
 
3.1%
2 3887
37.6%
3 2782
26.9%
4 2231
21.6%
5 686
 
6.6%
6 222
 
2.1%
7 93
 
0.9%
8 24
 
0.2%
9 16
 
0.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
11 5
 
< 0.1%
10 6
 
0.1%
9 16
 
0.2%
8 24
 
0.2%
7 93
 
0.9%
6 222
 
2.1%
5 686
6.6%

Depth
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45369296
Minimum0
Maximum85
Zeros6746
Zeros (%)65.2%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:47.284178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum85
Range85
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1053612
Coefficient of variation (CV)4.6404977
Kurtosis760.37519
Mean0.45369296
Median Absolute Deviation (MAD)0
Skewness25.498818
Sum4693
Variance4.4325456
MonotonicityNot monotonic
2023-09-21T16:29:47.619163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 6746
65.2%
1 3361
32.5%
2 183
 
1.8%
3 23
 
0.2%
4 7
 
0.1%
40 3
 
< 0.1%
5 3
 
< 0.1%
45 2
 
< 0.1%
70 2
 
< 0.1%
35 2
 
< 0.1%
Other values (10) 12
 
0.1%
ValueCountFrequency (%)
0 6746
65.2%
1 3361
32.5%
2 183
 
1.8%
3 23
 
0.2%
4 7
 
0.1%
5 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
21 1
 
< 0.1%
ValueCountFrequency (%)
85 1
 
< 0.1%
70 2
< 0.1%
60 2
< 0.1%
50 2
< 0.1%
45 2
< 0.1%
40 3
< 0.1%
35 2
< 0.1%
30 1
 
< 0.1%
28 1
 
< 0.1%
25 1
 
< 0.1%

Displacement
Real number (ℝ)

SKEWED  ZEROS 

Distinct1192
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66117.934
Minimum0
Maximum1 × 108
Zeros5828
Zeros (%)56.3%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:48.031189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34000
95-th percentile31000
Maximum1 × 108
Range1 × 108
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2409343.6
Coefficient of variation (CV)36.440092
Kurtosis1714.9657
Mean66117.934
Median Absolute Deviation (MAD)0
Skewness41.40589
Sum6.8392391 × 108
Variance5.8049367 × 1012
MonotonicityNot monotonic
2023-09-21T16:29:48.479275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5828
56.3%
12000 50
 
0.5%
14000 45
 
0.4%
1 44
 
0.4%
9000 43
 
0.4%
5000 43
 
0.4%
11000 42
 
0.4%
3000 40
 
0.4%
2 40
 
0.4%
10000 38
 
0.4%
Other values (1182) 4131
39.9%
ValueCountFrequency (%)
0 5828
56.3%
1 44
 
0.4%
2 40
 
0.4%
3 13
 
0.1%
4 10
 
0.1%
5 6
 
0.1%
6 9
 
0.1%
7 4
 
< 0.1%
8 12
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
100000000 6
0.1%
7100000 1
 
< 0.1%
3564880 1
 
< 0.1%
3432073 1
 
< 0.1%
1418000 1
 
< 0.1%
1281763 1
 
< 0.1%
1000000 1
 
< 0.1%
700000 1
 
< 0.1%
676913 1
 
< 0.1%
618850 1
 
< 0.1%

Number of Cabins
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3452243
Minimum-1
Maximum96
Zeros3869
Zeros (%)37.4%
Negative1
Negative (%)< 0.1%
Memory size40.5 KiB
2023-09-21T16:29:48.854294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum96
Range97
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9903131
Coefficient of variation (CV)1.47954
Kurtosis853.04073
Mean1.3452243
Median Absolute Deviation (MAD)1
Skewness21.645085
Sum13915
Variance3.9613462
MonotonicityNot monotonic
2023-09-21T16:29:49.199770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3869
37.4%
2 2419
23.4%
1 2100
20.3%
3 1369
 
13.2%
4 442
 
4.3%
5 98
 
0.9%
6 29
 
0.3%
8 5
 
< 0.1%
7 4
 
< 0.1%
-1 1
 
< 0.1%
Other values (8) 8
 
0.1%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 3869
37.4%
1 2100
20.3%
2 2419
23.4%
3 1369
 
13.2%
4 442
 
4.3%
5 98
 
0.9%
6 29
 
0.3%
7 4
 
< 0.1%
8 5
 
< 0.1%
ValueCountFrequency (%)
96 1
 
< 0.1%
74 1
 
< 0.1%
69 1
 
< 0.1%
52 1
 
< 0.1%
43 1
 
< 0.1%
18 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 5
< 0.1%
7 4
< 0.1%

Number of beds
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6917053
Minimum-23
Maximum266
Zeros3937
Zeros (%)38.1%
Negative3
Negative (%)< 0.1%
Memory size40.5 KiB
2023-09-21T16:29:49.521667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23
5-th percentile0
Q10
median2
Q34
95-th percentile7
Maximum266
Range289
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7729202
Coefficient of variation (CV)1.401684
Kurtosis2302.2249
Mean2.6917053
Median Absolute Deviation (MAD)2
Skewness33.637243
Sum27843
Variance14.234927
MonotonicityNot monotonic
2023-09-21T16:29:49.851469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 3937
38.1%
4 2391
23.1%
2 1375
 
13.3%
6 1330
 
12.9%
8 311
 
3.0%
3 241
 
2.3%
1 240
 
2.3%
5 234
 
2.3%
7 117
 
1.1%
10 66
 
0.6%
Other values (13) 102
 
1.0%
ValueCountFrequency (%)
-23 1
 
< 0.1%
-1 2
 
< 0.1%
0 3937
38.1%
1 240
 
2.3%
2 1375
 
13.3%
3 241
 
2.3%
4 2391
23.1%
5 234
 
2.3%
6 1330
 
12.9%
7 117
 
1.1%
ValueCountFrequency (%)
266 1
 
< 0.1%
69 1
 
< 0.1%
26 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 5
 
< 0.1%
14 7
 
0.1%
13 3
 
< 0.1%
12 40
0.4%
11 10
 
0.1%

Fuel Capacity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct717
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean965.35325
Minimum0
Maximum130000
Zeros3530
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:50.247008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median250
Q3940
95-th percentile3900
Maximum130000
Range130000
Interquartile range (IQR)940

Descriptive statistics

Standard deviation3090.8775
Coefficient of variation (CV)3.2018098
Kurtosis647.98079
Mean965.35325
Median Absolute Deviation (MAD)250
Skewness19.701346
Sum9985614
Variance9553523.5
MonotonicityNot monotonic
2023-09-21T16:29:50.687867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3530
34.1%
400 231
 
2.2%
1000 215
 
2.1%
800 194
 
1.9%
200 191
 
1.8%
500 180
 
1.7%
600 163
 
1.6%
300 157
 
1.5%
1200 143
 
1.4%
2000 132
 
1.3%
Other values (707) 5208
50.3%
ValueCountFrequency (%)
0 3530
34.1%
1 77
 
0.7%
2 74
 
0.7%
3 9
 
0.1%
4 4
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
8 4
 
< 0.1%
9 4
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
130000 1
< 0.1%
125700 1
< 0.1%
79000 1
< 0.1%
60000 1
< 0.1%
56000 1
< 0.1%
55000 1
< 0.1%
45333 1
< 0.1%
45000 1
< 0.1%
43000 1
< 0.1%
42000 1
< 0.1%

Engine Hours
Real number (ℝ)

ZEROS 

Distinct997
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean424.24874
Minimum0
Maximum32767
Zeros5211
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:51.103733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3555.5
95-th percentile1800
Maximum32767
Range32767
Interquartile range (IQR)555.5

Descriptive statistics

Standard deviation1004.1575
Coefficient of variation (CV)2.3669075
Kurtosis233.71132
Mean424.24874
Median Absolute Deviation (MAD)0
Skewness11.05107
Sum4388429
Variance1008332.4
MonotonicityNot monotonic
2023-09-21T16:29:51.523602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5211
50.4%
500 112
 
1.1%
700 101
 
1.0%
600 98
 
0.9%
650 88
 
0.9%
800 88
 
0.9%
300 87
 
0.8%
900 85
 
0.8%
400 75
 
0.7%
350 72
 
0.7%
Other values (987) 4327
41.8%
ValueCountFrequency (%)
0 5211
50.4%
1 29
 
0.3%
2 16
 
0.2%
3 7
 
0.1%
4 5
 
< 0.1%
5 28
 
0.3%
6 7
 
0.1%
7 3
 
< 0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
32767 1
< 0.1%
26200 1
< 0.1%
23431 1
< 0.1%
19500 1
< 0.1%
19000 1
< 0.1%
18100 1
< 0.1%
16000 1
< 0.1%
15275 1
< 0.1%
15000 1
< 0.1%
14633 1
< 0.1%

PriceClean
Real number (ℝ)

HIGH CORRELATION 

Distinct2651
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322181.18
Minimum0
Maximum31000000
Zeros44
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size40.5 KiB
2023-09-21T16:29:51.955986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13415
Q142270
median94775
Q3250000
95-th percentile1148325
Maximum31000000
Range31000000
Interquartile range (IQR)207730

Descriptive statistics

Standard deviation1022790.3
Coefficient of variation (CV)3.1745811
Kurtosis221.65938
Mean322181.18
Median Absolute Deviation (MAD)67562
Skewness12.205431
Sum3.3326422 × 109
Variance1.0461 × 1012
MonotonicityNot monotonic
2023-09-21T16:29:52.396867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65000 87
 
0.8%
45000 78
 
0.8%
35000 75
 
0.7%
89000 71
 
0.7%
55000 69
 
0.7%
75000 69
 
0.7%
85000 66
 
0.6%
95000 66
 
0.6%
99000 63
 
0.6%
79000 63
 
0.6%
Other values (2641) 9637
93.2%
ValueCountFrequency (%)
0 44
0.4%
3300 1
 
< 0.1%
3333 1
 
< 0.1%
3337 1
 
< 0.1%
3399 1
 
< 0.1%
3480 1
 
< 0.1%
3490 1
 
< 0.1%
3500 7
 
0.1%
3600 1
 
< 0.1%
3650 1
 
< 0.1%
ValueCountFrequency (%)
31000000 1
< 0.1%
24050000 1
< 0.1%
23500000 1
< 0.1%
19900000 1
< 0.1%
18900000 1
< 0.1%
17578125 1
< 0.1%
16900000 1
< 0.1%
16750000 2
< 0.1%
16025000 1
< 0.1%
14950000 1
< 0.1%

Price
Text

Distinct3283
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Memory size80.9 KiB
2023-09-21T16:29:52.974681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length12.544954
Min length10

Characters and Unicode

Total characters129765
Distinct characters36
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2185 ?
Unique (%)21.1%

Sample

1st rowCHF 3.337,-
2nd rowEUR 3.490,-
3rd rowCHF 3.770,-
4th rowDKK 25.900,-
5th rowSEK 35.000,-
ValueCountFrequency (%)
eur 8700
42.0%
chf 1036
 
5.0%
⣠306
 
1.5%
dkk 183
 
0.9%
65.000 87
 
0.4%
45.000 78
 
0.4%
35.000 75
 
0.4%
89.000 71
 
0.3%
75.000 69
 
0.3%
55.000 69
 
0.3%
Other values (2649) 10058
48.5%
2023-09-21T16:29:53.894927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27272
21.0%
. 10874
 
8.4%
10388
 
8.0%
, 10300
 
7.9%
- 10300
 
7.9%
U 8739
 
6.7%
E 8736
 
6.7%
R 8700
 
6.7%
9 6219
 
4.8%
5 5134
 
4.0%
Other values (26) 23103
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56693
43.7%
Uppercase Letter 30332
23.4%
Other Punctuation 21174
 
16.3%
Space Separator 10388
 
8.0%
Dash Punctuation 10300
 
7.9%
Lowercase Letter 572
 
0.4%
Currency Symbol 306
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 8739
28.8%
E 8736
28.8%
R 8700
28.7%
C 1036
 
3.4%
F 1036
 
3.4%
H 1036
 
3.4%
K 402
 
1.3%
 306
 
1.0%
D 222
 
0.7%
S 75
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 27272
48.1%
9 6219
 
11.0%
5 5134
 
9.1%
1 3873
 
6.8%
2 3233
 
5.7%
4 2713
 
4.8%
3 2526
 
4.5%
8 2035
 
3.6%
7 1868
 
3.3%
6 1820
 
3.2%
Lowercase Letter
ValueCountFrequency (%)
e 132
23.1%
r 88
15.4%
s 44
 
7.7%
t 44
 
7.7%
u 44
 
7.7%
q 44
 
7.7%
n 44
 
7.7%
o 44
 
7.7%
c 44
 
7.7%
i 44
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 10874
51.4%
, 10300
48.6%
Space Separator
ValueCountFrequency (%)
10388
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10300
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98861
76.2%
Latin 30904
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 8739
28.3%
E 8736
28.3%
R 8700
28.2%
C 1036
 
3.4%
F 1036
 
3.4%
H 1036
 
3.4%
K 402
 
1.3%
 306
 
1.0%
D 222
 
0.7%
e 132
 
0.4%
Other values (11) 559
 
1.8%
Common
ValueCountFrequency (%)
0 27272
27.6%
. 10874
 
11.0%
10388
 
10.5%
, 10300
 
10.4%
- 10300
 
10.4%
9 6219
 
6.3%
5 5134
 
5.2%
1 3873
 
3.9%
2 3233
 
3.3%
4 2713
 
2.7%
Other values (5) 8555
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129153
99.5%
None 612
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27272
21.1%
. 10874
 
8.4%
10388
 
8.0%
, 10300
 
8.0%
- 10300
 
8.0%
U 8739
 
6.8%
E 8736
 
6.8%
R 8700
 
6.7%
9 6219
 
4.8%
5 5134
 
4.0%
Other values (24) 22491
17.4%
None
ValueCountFrequency (%)
£ 306
50.0%
 306
50.0%
Distinct135
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size80.9 KiB
2023-09-21T16:29:54.324789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length42
Mean length10.521462
Min length3

Characters and Unicode

Total characters108834
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.7%

Sample

1st rowMotor Yacht
2nd rowCenter console boat
3rd rowSport Boat
4th rowSport Boat
5th rowClassic
ValueCountFrequency (%)
boat 3441
19.8%
yacht 3014
17.3%
motor 2857
16.4%
sport 1476
8.5%
flybridge 1228
 
7.0%
cabin 714
 
4.1%
trawler 699
 
4.0%
pilothouse 634
 
3.6%
hardtop 524
 
3.0%
console 388
 
2.2%
Other values (90) 2447
14.0%
2023-09-21T16:29:55.280583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 14797
13.6%
t 13063
 
12.0%
a 9700
 
8.9%
r 9199
 
8.5%
7078
 
6.5%
e 5057
 
4.6%
c 4058
 
3.7%
h 4029
 
3.7%
i 3665
 
3.4%
B 3496
 
3.2%
Other values (30) 34692
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 84179
77.3%
Uppercase Letter 17074
 
15.7%
Space Separator 7078
 
6.5%
Other Punctuation 503
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 14797
17.6%
t 13063
15.5%
a 9700
11.5%
r 9199
10.9%
e 5057
 
6.0%
c 4058
 
4.8%
h 4029
 
4.8%
i 3665
 
4.4%
l 3251
 
3.9%
b 2630
 
3.1%
Other values (11) 14730
17.5%
Uppercase Letter
ValueCountFrequency (%)
B 3496
20.5%
M 3058
17.9%
Y 3054
17.9%
S 1566
9.2%
F 1452
8.5%
C 1379
 
8.1%
P 786
 
4.6%
T 726
 
4.3%
H 704
 
4.1%
D 312
 
1.8%
Other values (6) 541
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 417
82.9%
/ 86
 
17.1%
Space Separator
ValueCountFrequency (%)
7078
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 101253
93.0%
Common 7581
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 14797
14.6%
t 13063
12.9%
a 9700
 
9.6%
r 9199
 
9.1%
e 5057
 
5.0%
c 4058
 
4.0%
h 4029
 
4.0%
i 3665
 
3.6%
B 3496
 
3.5%
l 3251
 
3.2%
Other values (27) 30938
30.6%
Common
ValueCountFrequency (%)
7078
93.4%
, 417
 
5.5%
/ 86
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 14797
13.6%
t 13063
 
12.0%
a 9700
 
8.9%
r 9199
 
8.5%
7078
 
6.5%
e 5057
 
4.6%
c 4058
 
3.7%
h 4029
 
3.7%
i 3665
 
3.4%
B 3496
 
3.2%
Other values (30) 34692
31.9%

Manufacturer
Text

MISSING 

Distinct932
Distinct (%)10.4%
Missing1390
Missing (%)13.4%
Memory size80.9 KiB
2023-09-21T16:29:55.794539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length20.558186
Min length14

Characters and Unicode

Total characters184078
Distinct characters70
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique398 ?
Unique (%)4.4%

Sample

1st rowRigiflex power boats
2nd rowTerhi power boats
3rd rowMarine power boats
4th rowPioner power boats
5th rowLinder power boats
ValueCountFrequency (%)
boats 9040
31.5%
power 8954
31.2%
bã©nã©teau 663
 
2.3%
jeanneau 553
 
1.9%
sunseeker 391
 
1.4%
marine 298
 
1.0%
quicksilver 297
 
1.0%
sea 256
 
0.9%
ray 253
 
0.9%
princess 248
 
0.9%
Other values (997) 7737
27.0%
2023-09-21T16:29:56.783770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 20495
11.1%
19750
10.7%
e 17509
9.5%
a 17369
9.4%
r 14798
 
8.0%
s 12514
 
6.8%
t 12325
 
6.7%
b 9419
 
5.1%
p 9388
 
5.1%
w 9225
 
5.0%
Other values (60) 41286
22.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149906
81.4%
Space Separator 19750
 
10.7%
Uppercase Letter 12584
 
6.8%
Other Symbol 1330
 
0.7%
Open Punctuation 194
 
0.1%
Close Punctuation 194
 
0.1%
Dash Punctuation 36
 
< 0.1%
Other Number 24
 
< 0.1%
Other Punctuation 23
 
< 0.1%
Decimal Number 19
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1713
13.6%
S 1443
11.5%
à 1373
10.9%
C 898
 
7.1%
M 817
 
6.5%
P 787
 
6.3%
A 765
 
6.1%
R 735
 
5.8%
J 589
 
4.7%
F 559
 
4.4%
Other values (17) 2905
23.1%
Lowercase Letter
ValueCountFrequency (%)
o 20495
13.7%
e 17509
11.7%
a 17369
11.6%
r 14798
9.9%
s 12514
8.3%
t 12325
8.2%
b 9419
6.3%
p 9388
6.3%
w 9225
6.2%
n 6403
 
4.3%
Other values (16) 20461
13.6%
Other Punctuation
ValueCountFrequency (%)
. 15
65.2%
& 6
 
26.1%
¶ 2
 
8.7%
Other Symbol
ValueCountFrequency (%)
© 1329
99.9%
¦ 1
 
0.1%
Other Number
ValueCountFrequency (%)
² 18
75.0%
¼ 6
 
25.0%
Decimal Number
ValueCountFrequency (%)
2 17
89.5%
3 2
 
10.5%
Space Separator
ValueCountFrequency (%)
19750
100.0%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 194
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Currency Symbol
ValueCountFrequency (%)
¤ 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Control
ValueCountFrequency (%)
– 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 162490
88.3%
Common 21588
 
11.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 20495
12.6%
e 17509
10.8%
a 17369
10.7%
r 14798
9.1%
s 12514
 
7.7%
t 12325
 
7.6%
b 9419
 
5.8%
p 9388
 
5.8%
w 9225
 
5.7%
n 6403
 
3.9%
Other values (43) 33045
20.3%
Common
ValueCountFrequency (%)
19750
91.5%
© 1329
 
6.2%
( 194
 
0.9%
) 194
 
0.9%
- 36
 
0.2%
² 18
 
0.1%
2 17
 
0.1%
¤ 15
 
0.1%
. 15
 
0.1%
¼ 6
 
< 0.1%
Other values (7) 14
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 181332
98.5%
None 2746
 
1.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 20495
11.3%
19750
10.9%
e 17509
9.7%
a 17369
9.6%
r 14798
 
8.2%
s 12514
 
6.9%
t 12325
 
6.8%
b 9419
 
5.2%
p 9388
 
5.2%
w 9225
 
5.1%
Other values (51) 38540
21.3%
None
ValueCountFrequency (%)
à 1373
50.0%
© 1329
48.4%
² 18
 
0.7%
¤ 15
 
0.5%
¼ 6
 
0.2%
¶ 2
 
0.1%
¦ 1
 
< 0.1%
¸ 1
 
< 0.1%
– 1
 
< 0.1%

Model
Text

Distinct7458
Distinct (%)72.1%
Missing3
Missing (%)< 0.1%
Memory size80.9 KiB
2023-09-21T16:29:57.308600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length33
Mean length13.074267
Min length1

Characters and Unicode

Total characters135201
Distinct characters106
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6256 ?
Unique (%)60.5%

Sample

1st rowCAP 400
2nd row400 C
3rd row370 S - Aluboot
4th row10 Classic Special Edition
5th rowGullholmensnipa 21
ValueCountFrequency (%)
fly 443
 
1.6%
340
 
1.3%
sport 282
 
1.0%
open 275
 
1.0%
ht 220
 
0.8%
flyer 202
 
0.8%
fisher 199
 
0.7%
cruiser 197
 
0.7%
antares 195
 
0.7%
merry 184
 
0.7%
Other values (4554) 24391
90.6%
2023-09-21T16:29:58.272736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16705
 
12.4%
e 5646
 
4.2%
0 5290
 
3.9%
a 5166
 
3.8%
r 5155
 
3.8%
A 4166
 
3.1%
S 3975
 
2.9%
o 3882
 
2.9%
E 3793
 
2.8%
5 3747
 
2.8%
Other values (96) 77676
57.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47762
35.3%
Uppercase Letter 42777
31.6%
Decimal Number 25948
19.2%
Space Separator 16710
 
12.4%
Other Punctuation 1159
 
0.9%
Dash Punctuation 452
 
0.3%
Close Punctuation 120
 
0.1%
Open Punctuation 120
 
0.1%
Math Symbol 34
 
< 0.1%
Other Number 30
 
< 0.1%
Other values (5) 89
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4166
 
9.7%
S 3975
 
9.3%
E 3793
 
8.9%
R 3539
 
8.3%
C 3197
 
7.5%
T 2609
 
6.1%
O 1931
 
4.5%
I 1895
 
4.4%
N 1756
 
4.1%
L 1755
 
4.1%
Other values (18) 14161
33.1%
Lowercase Letter
ValueCountFrequency (%)
e 5646
11.8%
a 5166
10.8%
r 5155
10.8%
o 3882
 
8.1%
t 3729
 
7.8%
i 3521
 
7.4%
n 3107
 
6.5%
s 2388
 
5.0%
l 2252
 
4.7%
u 1835
 
3.8%
Other values (17) 11081
23.2%
Other Punctuation
ValueCountFrequency (%)
. 683
58.9%
, 139
 
12.0%
/ 96
 
8.3%
' 63
 
5.4%
" 57
 
4.9%
! 26
 
2.2%
# 21
 
1.8%
? 19
 
1.6%
% 16
 
1.4%
& 14
 
1.2%
Other values (4) 25
 
2.2%
Decimal Number
ValueCountFrequency (%)
0 5290
20.4%
5 3747
14.4%
2 3322
12.8%
3 2816
10.9%
4 2539
9.8%
1 2002
 
7.7%
6 1987
 
7.7%
8 1709
 
6.6%
7 1558
 
6.0%
9 978
 
3.8%
Control
ValueCountFrequency (%)
œ 5
23.8%
Ÿ 5
23.8%
„ 5
23.8%
€ 2
 
9.5%
“ 1
 
4.8%
‚ 1
 
4.8%
™ 1
 
4.8%
Â… 1
 
4.8%
Math Symbol
ValueCountFrequency (%)
+ 29
85.3%
| 2
 
5.9%
< 2
 
5.9%
¬ 1
 
2.9%
Other Symbol
ValueCountFrequency (%)
© 25
89.3%
® 2
 
7.1%
¦ 1
 
3.6%
Modifier Symbol
ValueCountFrequency (%)
¨ 8
80.0%
¸ 1
 
10.0%
` 1
 
10.0%
Space Separator
ValueCountFrequency (%)
16705
> 99.9%
  5
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
¤ 24
82.8%
Â¥ 5
 
17.2%
Dash Punctuation
ValueCountFrequency (%)
- 452
100.0%
Close Punctuation
ValueCountFrequency (%)
) 120
100.0%
Open Punctuation
ValueCountFrequency (%)
( 120
100.0%
Other Number
ValueCountFrequency (%)
¼ 30
100.0%
Other Letter
ValueCountFrequency (%)
º 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90540
67.0%
Common 44661
33.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5646
 
6.2%
a 5166
 
5.7%
r 5155
 
5.7%
A 4166
 
4.6%
S 3975
 
4.4%
o 3882
 
4.3%
E 3793
 
4.2%
t 3729
 
4.1%
R 3539
 
3.9%
i 3521
 
3.9%
Other values (46) 47968
53.0%
Common
ValueCountFrequency (%)
16705
37.4%
0 5290
 
11.8%
5 3747
 
8.4%
2 3322
 
7.4%
3 2816
 
6.3%
4 2539
 
5.7%
1 2002
 
4.5%
6 1987
 
4.4%
8 1709
 
3.8%
7 1558
 
3.5%
Other values (40) 2986
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134940
99.8%
None 261
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16705
 
12.4%
e 5646
 
4.2%
0 5290
 
3.9%
a 5166
 
3.8%
r 5155
 
3.8%
A 4166
 
3.1%
S 3975
 
2.9%
o 3882
 
2.9%
E 3793
 
2.8%
5 3747
 
2.8%
Other values (73) 77415
57.4%
None
ValueCountFrequency (%)
à 124
47.5%
¼ 30
 
11.5%
© 25
 
9.6%
¤ 24
 
9.2%
¶ 8
 
3.1%
¨ 8
 
3.1%
œ 5
 
1.9%
  5
 
1.9%
Ÿ 5
 
1.9%
Â¥ 5
 
1.9%
Other values (13) 22
 
8.4%

Type
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Memory size80.9 KiB
Used boat,Diesel
4197 
Used boat,Unleaded
1645 
Used boat
1526 
new boat from stock,Unleaded
1132 
new boat from stock
673 
Other values (31)
1166 

Length

Max length29
Median length28
Mean length17.405649
Min length2

Characters and Unicode

Total characters179957
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st rownew boat from stock
2nd rownew boat from stock
3rd rownew boat from stock
4th rownew boat from stock
5th rowUsed boat

Common Values

ValueCountFrequency (%)
Used boat,Diesel 4197
40.6%
Used boat,Unleaded 1645
 
15.9%
Used boat 1526
 
14.8%
new boat from stock,Unleaded 1132
 
10.9%
new boat from stock 673
 
6.5%
new boat from stock,Diesel 298
 
2.9%
new boat on order,Unleaded 149
 
1.4%
, ,Used boat,Unleaded 115
 
1.1%
, ,Used boat,Diesel 86
 
0.8%
Display Model,Unleaded 75
 
0.7%
Other values (26) 443
 
4.3%

Length

2023-09-21T16:29:58.701599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
used 7730
30.0%
boat,diesel 4311
16.7%
boat 3960
15.3%
new 2399
 
9.3%
from 2126
 
8.2%
boat,unleaded 1813
 
7.0%
stock,unleaded 1132
 
4.4%
stock 674
 
2.6%
417
 
1.6%
stock,diesel 298
 
1.2%
Other values (20) 942
 
3.7%

Most occurring characters

ValueCountFrequency (%)
e 26474
14.7%
15465
8.6%
o 15049
 
8.4%
s 14741
 
8.2%
d 14524
 
8.1%
a 13466
 
7.5%
t 12311
 
6.8%
U 10929
 
6.1%
b 10131
 
5.6%
, 8767
 
4.9%
Other values (17) 38100
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 139731
77.6%
Uppercase Letter 15994
 
8.9%
Space Separator 15465
 
8.6%
Other Punctuation 8767
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26474
18.9%
o 15049
10.8%
s 14741
10.5%
d 14524
10.4%
a 13466
9.6%
t 12311
8.8%
b 10131
 
7.3%
l 8245
 
5.9%
n 5872
 
4.2%
i 4927
 
3.5%
Other values (8) 13991
10.0%
Uppercase Letter
ValueCountFrequency (%)
U 10929
68.3%
D 4869
30.4%
M 121
 
0.8%
E 56
 
0.4%
G 16
 
0.1%
H 2
 
< 0.1%
P 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
15465
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8767
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 155725
86.5%
Common 24232
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26474
17.0%
o 15049
9.7%
s 14741
9.5%
d 14524
9.3%
a 13466
8.6%
t 12311
7.9%
U 10929
7.0%
b 10131
 
6.5%
l 8245
 
5.3%
n 5872
 
3.8%
Other values (15) 23983
15.4%
Common
ValueCountFrequency (%)
15465
63.8%
, 8767
36.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 179957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 26474
14.7%
15465
8.6%
o 15049
 
8.4%
s 14741
 
8.2%
d 14524
 
8.1%
a 13466
 
7.5%
t 12311
 
6.8%
U 10929
 
6.1%
b 10131
 
5.6%
, 8767
 
4.9%
Other values (17) 38100
21.2%

Material
Categorical

IMBALANCE  MISSING 

Distinct11
Distinct (%)0.1%
Missing1832
Missing (%)17.7%
Memory size80.9 KiB
GRP
5754 
PVC
1156 
Steel
978 
Wood
 
245
Aluminium
 
237
Other values (6)
 
142

Length

Max length19
Median length3
Mean length3.5230263
Min length3

Characters and Unicode

Total characters29988
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowThermoplastic
2nd rowAluminium
3rd rowAluminium
4th rowAluminium
5th rowAluminium

Common Values

ValueCountFrequency (%)
GRP 5754
55.6%
PVC 1156
 
11.2%
Steel 978
 
9.5%
Wood 245
 
2.4%
Aluminium 237
 
2.3%
Plastic 88
 
0.9%
Carbon Fiber 32
 
0.3%
Thermoplastic 15
 
0.1%
Hypalon 5
 
< 0.1%
Reinforced concrete 1
 
< 0.1%
(Missing) 1832
 
17.7%

Length

2023-09-21T16:29:59.117751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
grp 5754
67.3%
pvc 1156
 
13.5%
steel 978
 
11.4%
wood 245
 
2.9%
aluminium 237
 
2.8%
plastic 88
 
1.0%
carbon 32
 
0.4%
fiber 32
 
0.4%
thermoplastic 15
 
0.2%
hypalon 5
 
0.1%
Other values (3) 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 6998
23.3%
R 5756
19.2%
G 5754
19.2%
e 2008
 
6.7%
l 1323
 
4.4%
C 1188
 
4.0%
V 1156
 
3.9%
t 1082
 
3.6%
S 978
 
3.3%
i 610
 
2.0%
Other values (20) 3135
10.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 22364
74.6%
Lowercase Letter 7591
 
25.3%
Space Separator 33
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2008
26.5%
l 1323
17.4%
t 1082
14.3%
i 610
 
8.0%
o 544
 
7.2%
m 489
 
6.4%
u 475
 
6.3%
n 276
 
3.6%
d 246
 
3.2%
a 140
 
1.8%
Other values (8) 398
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
P 6998
31.3%
R 5756
25.7%
G 5754
25.7%
C 1188
 
5.3%
V 1156
 
5.2%
S 978
 
4.4%
W 245
 
1.1%
A 237
 
1.1%
F 32
 
0.1%
T 15
 
0.1%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29955
99.9%
Common 33
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 6998
23.4%
R 5756
19.2%
G 5754
19.2%
e 2008
 
6.7%
l 1323
 
4.4%
C 1188
 
4.0%
V 1156
 
3.9%
t 1082
 
3.6%
S 978
 
3.3%
i 610
 
2.0%
Other values (19) 3102
10.4%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 6998
23.3%
R 5756
19.2%
G 5754
19.2%
e 2008
 
6.7%
l 1323
 
4.4%
C 1188
 
4.0%
V 1156
 
3.9%
t 1082
 
3.6%
S 978
 
3.3%
i 610
 
2.0%
Other values (20) 3135
10.5%

Engine
Text

MISSING 

Distinct4752
Distinct (%)49.8%
Missing809
Missing (%)7.8%
Memory size80.9 KiB
2023-09-21T16:29:59.762570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length73
Mean length16.543471
Min length1

Characters and Unicode

Total characters157742
Distinct characters105
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3991 ?
Unique (%)41.9%

Sample

1st row (Permission for Lake of Constance)
2nd rowVolvoPenta MD 2002
3rd row (Permission for Lake of Constance)
4th rowYamaha F8 BMH 5.6 kW
5th row1 x 15 HP / 11 kW
ValueCountFrequency (%)
volvo 2899
 
9.4%
penta 2313
 
7.5%
x 918
 
3.0%
mercruiser 860
 
2.8%
mercury 689
 
2.2%
hp 686
 
2.2%
2 643
 
2.1%
permission 639
 
2.1%
for 639
 
2.1%
lake 639
 
2.1%
Other values (2928) 19760
64.4%
2023-09-21T16:30:01.345966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21724
 
13.8%
e 8285
 
5.3%
o 8265
 
5.2%
r 6781
 
4.3%
a 6608
 
4.2%
n 5543
 
3.5%
P 4878
 
3.1%
V 4611
 
2.9%
0 4447
 
2.8%
i 4036
 
2.6%
Other values (95) 82564
52.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67607
42.9%
Uppercase Letter 44239
28.0%
Space Separator 21731
 
13.8%
Decimal Number 19890
 
12.6%
Other Punctuation 1829
 
1.2%
Dash Punctuation 936
 
0.6%
Open Punctuation 732
 
0.5%
Close Punctuation 725
 
0.5%
Control 19
 
< 0.1%
Math Symbol 14
 
< 0.1%
Other values (5) 20
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 4878
 
11.0%
V 4611
 
10.4%
M 4010
 
9.1%
A 2894
 
6.5%
C 2690
 
6.1%
D 2592
 
5.9%
L 2430
 
5.5%
E 2412
 
5.5%
T 2056
 
4.6%
O 2046
 
4.6%
Other values (18) 13620
30.8%
Lowercase Letter
ValueCountFrequency (%)
e 8285
12.3%
o 8265
12.2%
r 6781
10.0%
a 6608
9.8%
n 5543
 
8.2%
i 4036
 
6.0%
t 3794
 
5.6%
s 3625
 
5.4%
l 3526
 
5.2%
v 2623
 
3.9%
Other values (17) 14521
21.5%
Other Punctuation
ValueCountFrequency (%)
. 826
45.2%
/ 495
27.1%
, 450
24.6%
& 12
 
0.7%
; 9
 
0.5%
: 9
 
0.5%
? 8
 
0.4%
¡ 6
 
0.3%
# 3
 
0.2%
¶ 3
 
0.2%
Other values (5) 8
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 4447
22.4%
2 3162
15.9%
1 2516
12.6%
5 2014
10.1%
3 1880
9.5%
4 1784
9.0%
6 1571
 
7.9%
8 1034
 
5.2%
7 898
 
4.5%
9 584
 
2.9%
Control
ValueCountFrequency (%)
Ÿ 6
31.6%
€ 5
26.3%
“ 3
15.8%
œ 2
 
10.5%
ÂŽ 1
 
5.3%
„ 1
 
5.3%
— 1
 
5.3%
Currency Symbol
ValueCountFrequency (%)
¢ 2
33.3%
¤ 2
33.3%
$ 1
16.7%
Â¥ 1
16.7%
Math Symbol
ValueCountFrequency (%)
+ 12
85.7%
¬ 1
 
7.1%
| 1
 
7.1%
Other Symbol
ValueCountFrequency (%)
© 8
72.7%
° 2
 
18.2%
¦ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
21724
> 99.9%
  7
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 936
100.0%
Open Punctuation
ValueCountFrequency (%)
( 732
100.0%
Close Punctuation
ValueCountFrequency (%)
) 725
100.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 111846
70.9%
Common 45896
29.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8285
 
7.4%
o 8265
 
7.4%
r 6781
 
6.1%
a 6608
 
5.9%
n 5543
 
5.0%
P 4878
 
4.4%
V 4611
 
4.1%
i 4036
 
3.6%
M 4010
 
3.6%
t 3794
 
3.4%
Other values (45) 55035
49.2%
Common
ValueCountFrequency (%)
21724
47.3%
0 4447
 
9.7%
2 3162
 
6.9%
1 2516
 
5.5%
5 2014
 
4.4%
3 1880
 
4.1%
4 1784
 
3.9%
6 1571
 
3.4%
8 1034
 
2.3%
- 936
 
2.0%
Other values (40) 4828
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 157635
99.9%
None 107
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21724
 
13.8%
e 8285
 
5.3%
o 8265
 
5.2%
r 6781
 
4.3%
a 6608
 
4.2%
n 5543
 
3.5%
P 4878
 
3.1%
V 4611
 
2.9%
0 4447
 
2.8%
i 4036
 
2.6%
Other values (72) 82457
52.3%
None
ValueCountFrequency (%)
à 37
34.6%
 12
 
11.2%
© 8
 
7.5%
  7
 
6.5%
¡ 6
 
5.6%
Ÿ 6
 
5.6%
€ 5
 
4.7%
â 3
 
2.8%
“ 3
 
2.8%
¶ 3
 
2.8%
Other values (13) 17
15.9%

Engine Performance
Text

MISSING 

Distinct843
Distinct (%)10.5%
Missing2281
Missing (%)22.1%
Memory size80.9 KiB
2023-09-21T16:30:01.996951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length18.861094
Min length16

Characters and Unicode

Total characters152077
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique406 ?
Unique (%)5.0%

Sample

1st row1 x 18 HP / 13 kW
2nd row1 x 15 HP / 11 kW
3rd row1 x 2 HP / 1.5 kW
4th row1 x 130 HP / 96 kW
5th row1 x 1 HP / 0.7 kW
ValueCountFrequency (%)
kw 8063
14.3%
x 8063
14.3%
hp 8063
14.3%
8063
14.3%
2 4299
 
7.6%
1 3860
 
6.8%
221 505
 
0.9%
300 504
 
0.9%
147 471
 
0.8%
200 470
 
0.8%
Other values (879) 14080
24.9%
2023-09-21T16:30:03.091600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48378
31.8%
1 11773
 
7.7%
2 11539
 
7.6%
0 9273
 
6.1%
x 8063
 
5.3%
H 8063
 
5.3%
P 8063
 
5.3%
/ 8063
 
5.3%
k 8063
 
5.3%
W 8063
 
5.3%
Other values (8) 22736
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55123
36.2%
Space Separator 48378
31.8%
Uppercase Letter 24189
15.9%
Lowercase Letter 16126
 
10.6%
Other Punctuation 8261
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11773
21.4%
2 11539
20.9%
0 9273
16.8%
5 5089
9.2%
3 4331
 
7.9%
4 3440
 
6.2%
7 2983
 
5.4%
6 2376
 
4.3%
8 2363
 
4.3%
9 1956
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
H 8063
33.3%
P 8063
33.3%
W 8063
33.3%
Lowercase Letter
ValueCountFrequency (%)
x 8063
50.0%
k 8063
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 8063
97.6%
. 198
 
2.4%
Space Separator
ValueCountFrequency (%)
48378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111762
73.5%
Latin 40315
 
26.5%

Most frequent character per script

Common
ValueCountFrequency (%)
48378
43.3%
1 11773
 
10.5%
2 11539
 
10.3%
0 9273
 
8.3%
/ 8063
 
7.2%
5 5089
 
4.6%
3 4331
 
3.9%
4 3440
 
3.1%
7 2983
 
2.7%
6 2376
 
2.1%
Other values (3) 4517
 
4.0%
Latin
ValueCountFrequency (%)
x 8063
20.0%
H 8063
20.0%
P 8063
20.0%
k 8063
20.0%
W 8063
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48378
31.8%
1 11773
 
7.7%
2 11539
 
7.6%
0 9273
 
6.1%
x 8063
 
5.3%
H 8063
 
5.3%
P 8063
 
5.3%
/ 8063
 
5.3%
k 8063
 
5.3%
W 8063
 
5.3%
Other values (8) 22736
15.0%

Fuel Type
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)0.1%
Missing2322
Missing (%)22.4%
Memory size80.9 KiB
Diesel
4748 
Unleaded
3199 
Electric
 
56
Gas
 
16
Hybrid
 
2

Length

Max length8
Median length6
Mean length6.8056594
Min length3

Characters and Unicode

Total characters54595
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnleaded
2nd rowUnleaded
3rd rowElectric
4th rowUnleaded
5th rowUnleaded

Common Values

ValueCountFrequency (%)
Diesel 4748
45.9%
Unleaded 3199
30.9%
Electric 56
 
0.5%
Gas 16
 
0.2%
Hybrid 2
 
< 0.1%
Propane 1
 
< 0.1%
(Missing) 2322
22.4%

Length

2023-09-21T16:30:03.522467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T16:30:03.914342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
diesel 4748
59.2%
unleaded 3199
39.9%
electric 56
 
0.7%
gas 16
 
0.2%
hybrid 2
 
< 0.1%
propane 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 15951
29.2%
l 8003
14.7%
d 6400
11.7%
i 4806
 
8.8%
s 4764
 
8.7%
D 4748
 
8.7%
a 3216
 
5.9%
n 3200
 
5.9%
U 3199
 
5.9%
c 112
 
0.2%
Other values (10) 196
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 46573
85.3%
Uppercase Letter 8022
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15951
34.2%
l 8003
17.2%
d 6400
13.7%
i 4806
 
10.3%
s 4764
 
10.2%
a 3216
 
6.9%
n 3200
 
6.9%
c 112
 
0.2%
r 59
 
0.1%
t 56
 
0.1%
Other values (4) 6
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
D 4748
59.2%
U 3199
39.9%
E 56
 
0.7%
G 16
 
0.2%
H 2
 
< 0.1%
P 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 54595
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15951
29.2%
l 8003
14.7%
d 6400
11.7%
i 4806
 
8.8%
s 4764
 
8.7%
D 4748
 
8.7%
a 3216
 
5.9%
n 3200
 
5.9%
U 3199
 
5.9%
c 112
 
0.2%
Other values (10) 196
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 15951
29.2%
l 8003
14.7%
d 6400
11.7%
i 4806
 
8.8%
s 4764
 
8.7%
D 4748
 
8.7%
a 3216
 
5.9%
n 3200
 
5.9%
U 3199
 
5.9%
c 112
 
0.2%
Other values (10) 196
 
0.4%
Distinct3177
Distinct (%)30.8%
Missing43
Missing (%)0.4%
Memory size80.9 KiB
2023-09-21T16:30:04.409187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length85
Median length65
Mean length29.342006
Min length2

Characters and Unicode

Total characters302252
Distinct characters118
Distinct categories18 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2126 ?
Unique (%)20.6%

Sample

1st rowSwitzerland » Lake Geneva » Vésenaz
2nd rowGermany » Bönningstedt
3rd rowSwitzerland » Lake of Zurich » Stäfa ZH
4th rowDenmark » Svendborg
5th rowNordsee » Västra Frölunda
ValueCountFrequency (%)
â» 12700
27.2%
germany 1998
 
4.3%
france 1901
 
4.1%
italy 1875
 
4.0%
switzerland 1175
 
2.5%
netherlands 1099
 
2.4%
croatia 864
 
1.9%
hrvatska 828
 
1.8%
spain 725
 
1.6%
lake 580
 
1.2%
Other values (2473) 22957
49.2%
2023-09-21T16:30:05.385872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37350
 
12.4%
a 27062
 
9.0%
e 23344
 
7.7%
n 18330
 
6.1%
r 17962
 
5.9%
 12711
 
4.2%
» 12700
 
4.2%
i 12204
 
4.0%
t 12044
 
4.0%
l 10335
 
3.4%
Other values (108) 118210
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 184326
61.0%
Uppercase Letter 59960
 
19.8%
Space Separator 37351
 
12.4%
Final Punctuation 12700
 
4.2%
Other Punctuation 2586
 
0.9%
Decimal Number 1145
 
0.4%
Open Punctuation 1137
 
0.4%
Close Punctuation 1137
 
0.4%
Dash Punctuation 1032
 
0.3%
Currency Symbol 305
 
0.1%
Other values (8) 573
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
 12711
21.2%
S 4164
 
6.9%
I 3720
 
6.2%
G 3565
 
5.9%
N 3238
 
5.4%
L 3101
 
5.2%
F 2773
 
4.6%
A 2664
 
4.4%
C 2552
 
4.3%
E 2396
 
4.0%
Other values (19) 19076
31.8%
Lowercase Letter
ValueCountFrequency (%)
a 27062
14.7%
e 23344
12.7%
n 18330
9.9%
r 17962
9.7%
i 12204
 
6.6%
t 12044
 
6.5%
l 10335
 
5.6%
o 8992
 
4.9%
s 6928
 
3.8%
d 6355
 
3.4%
Other values (17) 40770
22.1%
Control
ValueCountFrequency (%)
œ 19
27.5%
– 13
18.8%
Ÿ 13
18.8%
˜ 6
 
8.7%
5
 
7.2%
‰ 4
 
5.8%
„ 2
 
2.9%
Â… 2
 
2.9%
€ 1
 
1.4%
† 1
 
1.4%
Other values (3) 3
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 1745
67.5%
/ 377
 
14.6%
¶ 148
 
5.7%
. 116
 
4.5%
' 97
 
3.8%
? 55
 
2.1%
& 21
 
0.8%
" 16
 
0.6%
¡ 6
 
0.2%
; 2
 
0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 212
18.5%
2 192
16.8%
1 155
13.5%
4 117
10.2%
3 106
9.3%
8 90
7.9%
5 84
 
7.3%
6 67
 
5.9%
7 63
 
5.5%
9 59
 
5.2%
Modifier Symbol
ValueCountFrequency (%)
´ 64
56.6%
¨ 38
33.6%
¸ 8
 
7.1%
¯ 2
 
1.8%
` 1
 
0.9%
Currency Symbol
ValueCountFrequency (%)
¤ 262
85.9%
¢ 30
 
9.8%
£ 7
 
2.3%
Â¥ 6
 
2.0%
Other Number
ValueCountFrequency (%)
¼ 256
90.1%
³ 27
 
9.5%
² 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 5
62.5%
| 2
 
25.0%
± 1
 
12.5%
Space Separator
ValueCountFrequency (%)
37350
> 99.9%
  1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
© 44
60.3%
¦ 29
39.7%
Other Letter
ValueCountFrequency (%)
ª 7
70.0%
º 3
30.0%
Final Punctuation
ValueCountFrequency (%)
» 12700
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1032
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 15
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 244296
80.8%
Common 57956
 
19.2%

Most frequent character per script

Common
ValueCountFrequency (%)
37350
64.4%
» 12700
 
21.9%
, 1745
 
3.0%
( 1137
 
2.0%
) 1137
 
2.0%
- 1032
 
1.8%
/ 377
 
0.7%
¤ 262
 
0.5%
¼ 256
 
0.4%
0 212
 
0.4%
Other values (50) 1748
 
3.0%
Latin
ValueCountFrequency (%)
a 27062
 
11.1%
e 23344
 
9.6%
n 18330
 
7.5%
r 17962
 
7.4%
 12711
 
5.2%
i 12204
 
5.0%
t 12044
 
4.9%
l 10335
 
4.2%
o 8992
 
3.7%
s 6928
 
2.8%
Other values (48) 94384
38.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 274815
90.9%
None 27437
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37350
 
13.6%
a 27062
 
9.8%
e 23344
 
8.5%
n 18330
 
6.7%
r 17962
 
6.5%
i 12204
 
4.4%
t 12044
 
4.4%
l 10335
 
3.8%
o 8992
 
3.3%
s 6928
 
2.5%
Other values (71) 100264
36.5%
None
ValueCountFrequency (%)
 12711
46.3%
» 12700
46.3%
à 1004
 
3.7%
¤ 262
 
1.0%
¼ 256
 
0.9%
¶ 148
 
0.5%
´ 64
 
0.2%
© 44
 
0.2%
¨ 38
 
0.1%
¢ 30
 
0.1%
Other values (27) 180
 
0.7%
Distinct680
Distinct (%)6.8%
Missing363
Missing (%)3.5%
Memory size80.9 KiB
2023-09-21T16:30:06.060656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.5598637
Min length2

Characters and Unicode

Total characters25550
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)2.0%

Sample

1st row226
2nd row75
3rd row124
4th row64
5th row131
ValueCountFrequency (%)
68 91
 
0.9%
74 88
 
0.9%
67 86
 
0.9%
62 85
 
0.9%
81 85
 
0.9%
73 84
 
0.8%
69 83
 
0.8%
76 81
 
0.8%
54 81
 
0.8%
75 80
 
0.8%
Other values (670) 9137
91.5%
2023-09-21T16:30:07.139308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5394
21.1%
2 2864
11.2%
3 2343
9.2%
4 2310
9.0%
6 2265
8.9%
5 2204
8.6%
7 2190
8.6%
8 2064
 
8.1%
9 1936
 
7.6%
0 1934
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25504
99.8%
Other Punctuation 46
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5394
21.1%
2 2864
11.2%
3 2343
9.2%
4 2310
9.1%
6 2265
8.9%
5 2204
8.6%
7 2190
8.6%
8 2064
 
8.1%
9 1936
 
7.6%
0 1934
 
7.6%
Other Punctuation
ValueCountFrequency (%)
' 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25550
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5394
21.1%
2 2864
11.2%
3 2343
9.2%
4 2310
9.0%
6 2265
8.9%
5 2204
8.6%
7 2190
8.6%
8 2064
 
8.1%
9 1936
 
7.6%
0 1934
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5394
21.1%
2 2864
11.2%
3 2343
9.2%
4 2310
9.0%
6 2265
8.9%
5 2204
8.6%
7 2190
8.6%
8 2064
 
8.1%
9 1936
 
7.6%
0 1934
 
7.6%

Comments
Text

MISSING 

Distinct6631
Distinct (%)93.7%
Missing3266
Missing (%)31.6%
Memory size80.9 KiB
2023-09-21T16:30:07.897954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32759
Median length3073
Mean length1777.0006
Min length1

Characters and Unicode

Total characters12577610
Distinct characters158
Distinct categories19 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6501 ?
Unique (%)91.8%

Sample

1st rowIn den Farben weiß oder grün lieferbar.,,2 abschließbare Staufächer, 3 Sitzbänke, Spiegelplatte, Scheuerleiste, Selbstlenzventil. Wie Terhi 400 zusätzlich Seitensteuerkonsole mit mech. Steuerung (ohne Riemen, Dollen Dollenhalterungen).,,Zusätzliche Ausstattung:,,Das stabile und sichere Terhi 400 C ist mit einem Steuerstand ausgestattet und als Sport- und Freizeitboot für ein bis vier Personen auf Seen oder geschützten Meeresgebieten geeignet. Gleichzeitig ist das Boot ein guter Begleiter für Sportangler. Das Boot hat beste Fahreigenschaften über die komplette Geschwindigkeitsskala selbst mit einem kleinen Motor. Zudem ist es einfach zu slippen und zu trailern. Abschließbare Backskisten im Bug und Heck bieten darüber hinaus praktischen Stauraum. Sie können das Boot für Ihren Verwendungszweck mit Zubehör individualisieren, zum Beispiel mit einer Windschutzscheibe für den Steuerstand und Relingsätzen oder mit einer gepolsterten Liegefläche für den Bugbereich.,,Preis ab Werk zzgl. Vorfracht,,Gerne erstellen wir Ihnen ein persönliches Angebot und freuen uns auf,Ihre Kontaktaufnahme.,,Bilder können Sonderoptionen zeigen. Irrtum vorbehalten.,,,,www.gruendl.de
2nd rowMORSOM OG LETKØRT KVALITETSBÅD!,Nye Pioner 10 Classic er en snerten og sikker båd som er let at håndtere og manøvrere ? og som passer perfekt for barn og voksne.,,Den moderne model er selvdrenerende, solid, letplanende og fin at ro. Pioner 10 Classic føles vældig tryg og er samtidig egnet til mange forskellige brugsområder, nogle både store og små vil sætte pris på. Båden har plads til tre personer og er godkendt for 9.9 Hk.,,,,,,,Farver:,,Leif Larsens Special Edition version med lav konsol, ræling agter, badestige, Yamaha F9.9 Vmax Sport m. el-start og fjernbetjening, sejlklar tilbud Kr. 54.500,-!
3rd rowCrazy One´s elegante og spændende design kombinerer facinerende "motorcykel-egenskaber" med et dybt og tørt cockpit til 2 personer.,,Linieføring og udformning under vandlinien går Crazy One sjov og let at sejle, giver udfordring og spænding til race, vandski, fiskeri og som ledsagerfartøj - Den ideelle fritidsbåd for hele familien.,,Den lave vægt sikrer et sparsomt brændstofsforbrug og let håndtering på biltag, påhængsvogn, bådtrailer eller bag på en større motorbåd. Båden er udstyret med 15 hk 4-takts motor årgang 2010, sejlet minimalt af timer.
4th rowCaratteristiche tecniche,,Materiale: Polietilene Rotazionale,Motorizzazione: Gambo lungo,Lunghezza: 4,35 mt.,Larghezza: 1,73 mt.,Altezza: 0,81 mt.,Altezza specchio di poppa: 0,52 mt.,Peso: 184 kg.,Capacità (persone): CE Cat. D 12 pers. - Cat. C 8 pers,Peso massimo: CE Cat. D 910 kg. - Cat C 690 kg. (compreso motore),Potenza massima: 30 CV (22 kw),Colorazioni: Blu, Arancione, Rosso, Grigio, Nero, Giallo e Bianco,,Le imbarcazioni Whaly sono realizzate interamente con doppia parete plastica (PE) in unico pezzo e prodotte mediante la tecnica dello stampaggio rotazionale.,La progettazione, il metodo di produzione e la plastica polietilene di elevata qualità costituiscono le basi per imbarcazioni ultraresistenti, inaffondabili, indistruttibili e con necessità di manutenzione minima, caratterizzate da grande spazio interno, sicurezza e stabilità.,,Tutti i modelli Whaly 210, 270, 310, 370, 435, 440 Classic e 500 si prestano ottimamente al settore ricreativo, mentre i modelli 310, 370, 435 e 500 sono multifunzionali e sono perfetti a fini professionali. Nei modelli Whaly ad uso professionale sono fondamentalii requisiti, spesso estremi, posti dal cliente: una scuola di vela, ad esempio, desidera un’imbarcazione guida stabile, sicura e resistente e nell’ambito della navigazione professionale le imbarcazioni Whaly sono utilizzate come mezzi di salvataggio a bordo, con certificazione 94/25/EG, 2003/44/EG e NEN-EN 1914 (opzionale).,,Nei modelli ad uso ricreativo, l’accento sempre posto sulla navigazione sicura e stabile, ma un occhio riservato anche allo styling, alla comodità d’uso e al comfort senza tralasciare sicurezza, affidabilità e durata.,,Grazie a simili caratteristiche, le imbarcazioni Whaly trovano diversi campi d’applicazione:,,Navigazione da diporto,Imbarcazione da lavoro,Natante di salvataggio,Pesca sportiva,Noleggio,Scuole nautiche,,Accessori optional: Consolle, panca con gavone, remi, rollbar, tendalino, cuscineria e telo copertura totale.
5th rowDecriptino:,Small open console boat with windshield, suntanning space in bow, driver´s seat. Storage locker bow, anchor storage, front console seat with backrest cushion.,Accessories/Equipment:,Upholstery – reling bow, clamps and cleats in stainless steel, reling stern seat,Possibility in package with HONDA 40CV 4stroke (year 2004) at,€ 5.500,00,Motorisation: without engine,Condition: only lake – hull very good condition,,Visible: Gardalake,,Price:,*plus commission.
ValueCountFrequency (%)
31943
 
1.9%
and 26211
 
1.6%
the 24287
 
1.4%
in 22726
 
1.4%
de 22046
 
1.3%
with 19060
 
1.1%
a 16333
 
1.0%
und 11811
 
0.7%
of 11436
 
0.7%
to 11290
 
0.7%
Other values (158549) 1484371
88.3%
2023-09-21T16:30:09.131563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1679035
 
13.3%
e 1177054
 
9.4%
a 773406
 
6.1%
i 728141
 
5.8%
n 719476
 
5.7%
t 694916
 
5.5%
r 682383
 
5.4%
o 592927
 
4.7%
s 522491
 
4.2%
l 434076
 
3.5%
Other values (148) 4573705
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8953212
71.2%
Space Separator 1693405
 
13.5%
Uppercase Letter 976738
 
7.8%
Other Punctuation 490330
 
3.9%
Decimal Number 274086
 
2.2%
Dash Punctuation 62545
 
0.5%
Other Symbol 23889
 
0.2%
Other Number 21301
 
0.2%
Open Punctuation 15496
 
0.1%
Close Punctuation 15483
 
0.1%
Other values (9) 51125
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 89728
 
9.2%
à 84060
 
8.6%
E 64258
 
6.6%
A 64138
 
6.6%
T 58961
 
6.0%
C 53686
 
5.5%
D 44907
 
4.6%
B 44844
 
4.6%
P 41462
 
4.2%
R 41288
 
4.2%
Other values (20) 389406
39.9%
Lowercase Letter
ValueCountFrequency (%)
e 1177054
13.1%
a 773406
 
8.6%
i 728141
 
8.1%
n 719476
 
8.0%
t 694916
 
7.8%
r 682383
 
7.6%
o 592927
 
6.6%
s 522491
 
5.8%
l 434076
 
4.8%
c 345291
 
3.9%
Other values (19) 2283051
25.5%
Control
ValueCountFrequency (%)
€ 4334
29.9%
Ÿ 3607
24.9%
2878
19.9%
– 771
 
5.3%
œ 708
 
4.9%
“ 601
 
4.2%
„ 435
 
3.0%
™ 274
 
1.9%
‚ 261
 
1.8%
‰ 234
 
1.6%
Other values (16) 371
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 283644
57.8%
. 82548
 
16.8%
: 47561
 
9.7%
/ 16027
 
3.3%
? 9695
 
2.0%
\ 8464
 
1.7%
# 7719
 
1.6%
' 7199
 
1.5%
& 4930
 
1.0%
" 4688
 
1.0%
Other values (10) 17855
 
3.6%
Decimal Number
ValueCountFrequency (%)
0 67244
24.5%
2 56017
20.4%
1 43286
15.8%
3 20060
 
7.3%
5 20010
 
7.3%
4 19093
 
7.0%
6 14129
 
5.2%
8 12490
 
4.6%
9 11258
 
4.1%
7 10499
 
3.8%
Math Symbol
ValueCountFrequency (%)
+ 5002
42.8%
> 4637
39.7%
± 1416
 
12.1%
¬ 278
 
2.4%
= 219
 
1.9%
| 114
 
1.0%
~ 14
 
0.1%
< 12
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
¨ 5186
83.6%
¸ 568
 
9.2%
´ 395
 
6.4%
` 40
 
0.6%
¯ 9
 
0.1%
^ 2
 
< 0.1%
Other Number
ValueCountFrequency (%)
¼ 15274
71.7%
³ 5214
 
24.5%
² 628
 
2.9%
¹ 177
 
0.8%
½ 8
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
¤ 10635
69.6%
¢ 3311
 
21.7%
Â¥ 1152
 
7.5%
£ 168
 
1.1%
$ 4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
© 22634
94.7%
¦ 706
 
3.0%
° 382
 
1.6%
® 167
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 15474
99.9%
[ 20
 
0.1%
{ 2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 15461
99.9%
] 19
 
0.1%
} 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1679035
99.2%
  14370
 
0.8%
Other Letter
ValueCountFrequency (%)
ª 850
58.9%
º 593
41.1%
Dash Punctuation
ValueCountFrequency (%)
- 62545
100.0%
Format
ValueCountFrequency (%)
­ 1143
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 623
100.0%
Final Punctuation
ValueCountFrequency (%)
» 151
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9931391
79.0%
Common 2646219
 
21.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1679035
63.5%
, 283644
 
10.7%
. 82548
 
3.1%
0 67244
 
2.5%
- 62545
 
2.4%
2 56017
 
2.1%
: 47561
 
1.8%
1 43286
 
1.6%
© 22634
 
0.9%
3 20060
 
0.8%
Other values (88) 281645
 
10.6%
Latin
ValueCountFrequency (%)
e 1177054
 
11.9%
a 773406
 
7.8%
i 728141
 
7.3%
n 719476
 
7.2%
t 694916
 
7.0%
r 682383
 
6.9%
o 592927
 
6.0%
s 522491
 
5.3%
l 434076
 
4.4%
c 345291
 
3.5%
Other values (50) 3261230
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12372615
98.4%
None 204995
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1679035
 
13.6%
e 1177054
 
9.5%
a 773406
 
6.3%
i 728141
 
5.9%
n 719476
 
5.8%
t 694916
 
5.6%
r 682383
 
5.5%
o 592927
 
4.8%
s 522491
 
4.2%
l 434076
 
3.5%
Other values (86) 4368710
35.3%
None
ValueCountFrequency (%)
à 84060
41.0%
© 22634
 
11.0%
¼ 15274
 
7.5%
  14370
 
7.0%
 11573
 
5.6%
¤ 10635
 
5.2%
³ 5214
 
2.5%
¨ 5186
 
2.5%
â 4571
 
2.2%
¶ 4449
 
2.2%
Other values (52) 27029
 
13.2%

Equipment
Text

MISSING 

Distinct5301
Distinct (%)85.9%
Missing4170
Missing (%)40.3%
Memory size80.9 KiB
2023-09-21T16:30:09.551428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length798
Median length520
Mean length176.06608
Min length3

Characters and Unicode

Total characters1087032
Distinct characters86
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4933 ?
Unique (%)79.9%

Sample

1st rowAnchor,Battery,Bilge Pump,Bilge pump,Cockpit Cover,Compass,Depth Instrument,Gas Stove,Stern Thruster,Teak Cockpit,Teak Deck,Underwater Paint
2nd rowAnchor,Mooring Cover,Swim Ladder,Trailer
3rd rowFull Enclosure
4th rowCompass,Fire Extinguisher,Full Enclosure,Radio,Speed Instrument,Swim Ladder
5th rowBattery
ValueCountFrequency (%)
charger,bilge 2685
 
4.3%
instrument,fm 1898
 
3.0%
pump,bimini 1591
 
2.5%
air 1530
 
2.4%
radio,fire 1324
 
2.1%
shower,depth 1278
 
2.0%
anchor 1210
 
1.9%
thruster,cd 1058
 
1.7%
ladder,teak 1013
 
1.6%
instrument 964
 
1.5%
Other values (4779) 48242
76.8%
2023-09-21T16:30:10.553616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 100077
 
9.2%
r 89155
 
8.2%
, 88455
 
8.1%
t 78671
 
7.2%
o 63654
 
5.9%
i 63349
 
5.8%
a 59677
 
5.5%
56621
 
5.2%
n 50096
 
4.6%
s 29463
 
2.7%
Other values (76) 407814
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 775634
71.4%
Uppercase Letter 164973
 
15.2%
Other Punctuation 88631
 
8.2%
Space Separator 56621
 
5.2%
Decimal Number 622
 
0.1%
Dash Punctuation 249
 
< 0.1%
Other Symbol 98
 
< 0.1%
Control 69
 
< 0.1%
Other Number 54
 
< 0.1%
Currency Symbol 47
 
< 0.1%
Other values (5) 34
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 100077
12.9%
r 89155
11.5%
t 78671
10.1%
o 63654
 
8.2%
i 63349
 
8.2%
a 59677
 
7.7%
n 50096
 
6.5%
s 29463
 
3.8%
l 26829
 
3.5%
p 26460
 
3.4%
Other values (16) 188203
24.3%
Uppercase Letter
ValueCountFrequency (%)
C 22395
13.6%
B 20100
12.2%
S 17012
10.3%
P 15433
9.4%
T 11721
 
7.1%
R 10735
 
6.5%
D 9843
 
6.0%
F 8977
 
5.4%
A 8495
 
5.1%
I 7799
 
4.7%
Other values (16) 32463
19.7%
Decimal Number
ValueCountFrequency (%)
3 464
74.6%
2 61
 
9.8%
0 32
 
5.1%
1 26
 
4.2%
5 13
 
2.1%
4 11
 
1.8%
9 6
 
1.0%
8 5
 
0.8%
6 3
 
0.5%
7 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 88455
99.8%
/ 58
 
0.1%
. 54
 
0.1%
¶ 31
 
< 0.1%
' 24
 
< 0.1%
& 6
 
< 0.1%
% 2
 
< 0.1%
: 1
 
< 0.1%
Control
ValueCountFrequency (%)
Ÿ 32
46.4%
‰ 23
33.3%
– 14
20.3%
Other Symbol
ValueCountFrequency (%)
© 97
99.0%
® 1
 
1.0%
Currency Symbol
ValueCountFrequency (%)
¤ 44
93.6%
¢ 3
 
6.4%
Modifier Symbol
ValueCountFrequency (%)
¨ 7
50.0%
´ 7
50.0%
Space Separator
ValueCountFrequency (%)
56621
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 249
100.0%
Other Number
ValueCountFrequency (%)
¼ 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Letter
ValueCountFrequency (%)
ª 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 940608
86.5%
Common 146424
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 100077
 
10.6%
r 89155
 
9.5%
t 78671
 
8.4%
o 63654
 
6.8%
i 63349
 
6.7%
a 59677
 
6.3%
n 50096
 
5.3%
s 29463
 
3.1%
l 26829
 
2.9%
p 26460
 
2.8%
Other values (43) 353177
37.5%
Common
ValueCountFrequency (%)
, 88455
60.4%
56621
38.7%
3 464
 
0.3%
- 249
 
0.2%
© 97
 
0.1%
2 61
 
< 0.1%
/ 58
 
< 0.1%
. 54
 
< 0.1%
¼ 54
 
< 0.1%
¤ 44
 
< 0.1%
Other values (23) 267
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1086404
99.9%
None 628
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 100077
 
9.2%
r 89155
 
8.2%
, 88455
 
8.1%
t 78671
 
7.2%
o 63654
 
5.9%
i 63349
 
5.8%
a 59677
 
5.5%
56621
 
5.2%
n 50096
 
4.6%
s 29463
 
2.7%
Other values (62) 407186
37.5%
None
ValueCountFrequency (%)
à 313
49.8%
© 97
 
15.4%
¼ 54
 
8.6%
¤ 44
 
7.0%
Ÿ 32
 
5.1%
¶ 31
 
4.9%
‰ 23
 
3.7%
– 14
 
2.2%
¨ 7
 
1.1%
´ 7
 
1.1%
Other values (4) 6
 
1.0%

Currency
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing44
Missing (%)0.4%
Memory size80.9 KiB
EUR
8700 
CHF
1036 
£
 
306
DKK
 
183
USD
 
39

Length

Max length3
Median length3
Mean length2.9405825
Min length1

Characters and Unicode

Total characters30288
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHF
2nd rowEUR
3rd rowCHF
4th rowDKK
5th rowSEK

Common Values

ValueCountFrequency (%)
EUR 8700
84.1%
CHF 1036
 
10.0%
£ 306
 
3.0%
DKK 183
 
1.8%
USD 39
 
0.4%
SEK 36
 
0.3%
(Missing) 44
 
0.4%

Length

2023-09-21T16:30:11.001472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-21T16:30:11.356357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
eur 8700
84.5%
chf 1036
 
10.1%
£ 306
 
3.0%
dkk 183
 
1.8%
usd 39
 
0.4%
sek 36
 
0.3%

Most occurring characters

ValueCountFrequency (%)
U 8739
28.9%
E 8736
28.8%
R 8700
28.7%
C 1036
 
3.4%
H 1036
 
3.4%
F 1036
 
3.4%
K 402
 
1.3%
£ 306
 
1.0%
D 222
 
0.7%
S 75
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29982
99.0%
Currency Symbol 306
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 8739
29.1%
E 8736
29.1%
R 8700
29.0%
C 1036
 
3.5%
H 1036
 
3.5%
F 1036
 
3.5%
K 402
 
1.3%
D 222
 
0.7%
S 75
 
0.3%
Currency Symbol
ValueCountFrequency (%)
£ 306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29982
99.0%
Common 306
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 8739
29.1%
E 8736
29.1%
R 8700
29.0%
C 1036
 
3.5%
H 1036
 
3.5%
F 1036
 
3.5%
K 402
 
1.3%
D 222
 
0.7%
S 75
 
0.3%
Common
ValueCountFrequency (%)
£ 306
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29982
99.0%
None 306
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 8739
29.1%
E 8736
29.1%
R 8700
29.0%
C 1036
 
3.5%
H 1036
 
3.5%
F 1036
 
3.5%
K 402
 
1.3%
D 222
 
0.7%
S 75
 
0.3%
None
ValueCountFrequency (%)
£ 306
100.0%

Amount_in_USD
Real number (ℝ)

HIGH CORRELATION 

Distinct3273
Distinct (%)31.8%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean325558.18
Minimum3185
Maximum33480000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2023-09-21T16:30:11.735235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3185
5-th percentile15012
Q145360
median97200
Q3264600
95-th percentile1074600
Maximum33480000
Range33476815
Interquartile range (IQR)219240

Descriptive statistics

Standard deviation1056426
Coefficient of variation (CV)3.2449685
Kurtosis251.92614
Mean325558.18
Median Absolute Deviation (MAD)68157.87
Skewness13.052964
Sum3.3532492 × 109
Variance1.1160359 × 1012
MonotonicityNot monotonic
2023-09-21T16:30:12.185091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70200 81
 
0.8%
48600 69
 
0.7%
96120 66
 
0.6%
81000 66
 
0.6%
37800 66
 
0.6%
59400 64
 
0.6%
106920 61
 
0.6%
91800 61
 
0.6%
102600 59
 
0.6%
85320 59
 
0.6%
Other values (3263) 9648
93.3%
ValueCountFrequency (%)
3185 1
 
< 0.1%
3472 1
 
< 0.1%
3564 1
 
< 0.1%
3599.64 1
 
< 0.1%
3626 1
 
< 0.1%
3670.92 1
 
< 0.1%
3769.2 1
 
< 0.1%
3770.81 1
 
< 0.1%
3780 5
< 0.1%
3932.4 1
 
< 0.1%
ValueCountFrequency (%)
33480000 1
< 0.1%
25974000 1
< 0.1%
25380000 1
< 0.1%
21492000 1
< 0.1%
18900000 1
< 0.1%
18252000 1
< 0.1%
18090000 2
< 0.1%
17307000 1
< 0.1%
16146000 1
< 0.1%
16038000 1
< 0.1%

Interactions

2023-09-21T16:29:38.538430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:06.709728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:10.023689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:13.124084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:16.381114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:19.374288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:22.510295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:25.584614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:28.914313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:32.186931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:35.359481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:38.859432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:07.041621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:10.325599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:13.423988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:16.676019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:19.695529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:22.814281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:25.890608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:29.229093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:32.495832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:35.672477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:39.142422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:07.328534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:10.595803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:13.689786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:16.939934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:19.969441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:23.082195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:26.385602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:29.530997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:32.776813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:35.949448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:39.424428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:07.615461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:10.867826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:13.953595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:17.199593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:20.242357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:23.350108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:26.656577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:29.835915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:33.051725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:36.227470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:39.698369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:07.895371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:11.123816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:14.209774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:17.445719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:20.503273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:23.610025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:26.920012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:30.111622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:33.318486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:36.495460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:40.278413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:08.192275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:11.403789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:14.488753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:17.717665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:20.782184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:23.889936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:27.214375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:30.430521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:33.617505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:36.780460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:40.565406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:08.483181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:11.685764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:14.757769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:17.979704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:21.059094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:24.155849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:27.483289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:30.716403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:33.898368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:37.063432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:40.853406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:08.773088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:11.955811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:15.027766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:18.243651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:21.334010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:24.424627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:27.751681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:30.999312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:34.171507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:37.355348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:41.196580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:09.079850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:12.248872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:15.506760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:18.518562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:21.624578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:24.711628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:28.038589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:31.284221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:34.462496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:37.650388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:41.505397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:09.383752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:12.532146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:15.792754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:18.794474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:21.911488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:24.994601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:28.322503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:31.577126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:34.750484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:37.942388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:41.808861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:09.693795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:12.822056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:16.081210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:19.077383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:22.206395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:25.283618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:28.613409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:31.876031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:35.048352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-21T16:29:38.234326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-21T16:30:12.541976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Year BuiltLengthWidthDepthDisplacementNumber of CabinsNumber of bedsFuel CapacityEngine HoursPriceCleanAmount_in_USDTypeMaterialFuel TypeCurrency
Year Built1.000-0.148-0.124-0.1340.027-0.073-0.090-0.117-0.2500.2000.1980.3460.1360.1670.092
Length-0.1481.0000.9060.6410.2410.5930.5780.5680.3180.7950.8190.2050.0860.3030.126
Width-0.1240.9061.0000.6290.2390.5700.5550.5460.3100.7450.7660.2730.1410.3450.149
Depth-0.1340.6410.6291.0000.2270.3910.4200.4620.2650.5130.5290.0510.0070.0750.006
Displacement0.0270.2410.2390.2271.0000.3110.1740.3240.0570.2610.2560.0000.0650.0000.000
Number of Cabins-0.0730.5930.5700.3910.3111.0000.6960.4850.3010.4750.5060.0000.0030.0000.000
Number of beds-0.0900.5780.5550.4200.1740.6961.0000.4620.3430.4280.4560.2100.0480.2740.090
Fuel Capacity-0.1170.5680.5460.4620.3240.4850.4621.0000.4080.4460.4780.0000.0400.0120.064
Engine Hours-0.2500.3180.3100.2650.0570.3010.3430.4081.0000.1520.1690.0000.1040.0280.012
PriceClean0.2000.7950.7450.5130.2610.4750.4280.4460.1521.0000.9770.0330.0000.0250.098
Amount_in_USD0.1980.8190.7660.5290.2560.5060.4560.4780.1690.9771.0000.0370.0140.0260.059
Type0.3460.2050.2730.0510.0000.0000.2100.0000.0000.0330.0371.0000.2460.9990.189
Material0.1360.0860.1410.0070.0650.0030.0480.0400.1040.0000.0140.2461.0000.1320.125
Fuel Type0.1670.3030.3450.0750.0000.0000.2740.0120.0280.0250.0260.9990.1321.0000.142
Currency0.0920.1260.1490.0060.0000.0000.0900.0640.0120.0980.0590.1890.1250.1421.000

Missing values

2023-09-21T16:29:42.341690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-21T16:29:43.390313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-21T16:29:44.194354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Year BuiltLengthWidthDepthDisplacementNumber of CabinsNumber of bedsFuel CapacityEngine HoursPriceCleanPriceBoat TypeManufacturerModelTypeMaterialEngineEngine PerformanceFuel TypeLocationNumber of views last 7 daysCommentsEquipmentCurrencyAmount_in_USD
02017410000003337CHF 3.337,-Motor YachtRigiflex power boatsCAP 400new boat from stockNaNNaNNaNNaNSwitzerland » Lake Geneva » Vésenaz226NaNNaNCHF3770.81
1202041015000003490EUR 3.490,-Center console boatTerhi power boats400 Cnew boat from stockThermoplasticNaNNaNNaNGermany » Bönningstedt75In den Farben weiß oder grün lieferbar.,,2 abschließbare Staufächer, 3 Sitzbänke, Spiegelplatte, Scheuerleiste, Selbstlenzventil. Wie Terhi 400 zusätzlich Seitensteuerkonsole mit mech. Steuerung (ohne Riemen, Dollen Dollenhalterungen).,,Zusätzliche Ausstattung:,,Das stabile und sichere Terhi 400 C ist mit einem Steuerstand ausgestattet und als Sport- und Freizeitboot für ein bis vier Personen auf Seen oder geschützten Meeresgebieten geeignet. Gleichzeitig ist das Boot ein guter Begleiter für Sportangler. Das Boot hat beste Fahreigenschaften über die komplette Geschwindigkeitsskala selbst mit einem kleinen Motor. Zudem ist es einfach zu slippen und zu trailern. Abschließbare Backskisten im Bug und Heck bieten darüber hinaus praktischen Stauraum. Sie können das Boot für Ihren Verwendungszweck mit Zubehör individualisieren, zum Beispiel mit einer Windschutzscheibe für den Steuerstand und Relingsätzen oder mit einer gepolsterten Liegefläche für den Bugbereich.,,Preis ab Werk zzgl. Vorfracht,,Gerne erstellen wir Ihnen ein persönliches Angebot und freuen uns auf,Ihre Kontaktaufnahme.,,Bilder können Sonderoptionen zeigen. Irrtum vorbehalten.,,,,www.gruendl.deNaNEUR3769.20
20310000003770CHF 3.770,-Sport BoatMarine power boats370 S - Alubootnew boat from stockAluminium(Permission for Lake of Constance)NaNNaNSwitzerland » Lake of Zurich » Stäfa ZH124NaNNaNCHF4260.10
32020310110000025900DKK 25.900,-Sport BoatPioner power boats10 Classic Special Editionnew boat from stockNaNNaNNaNNaNDenmark » Svendborg64MORSOM OG LETKØRT KVALITETSBÅD!,Nye Pioner 10 Classic er en snerten og sikker båd som er let at håndtere og manøvrere ? og som passer perfekt for barn og voksne.,,Den moderne model er selvdrenerende, solid, letplanende og fin at ro. Pioner 10 Classic føles vældig tryg og er samtidig egnet til mange forskellige brugsområder, nogle både store og små vil sætte pris på. Båden har plads til tre personer og er godkendt for 9.9 Hk.,,,,,,,Farver:,,Leif Larsens Special Edition version med lav konsol, ræling agter, badestige, Yamaha F9.9 Vmax Sport m. el-start og fjernbetjening, sejlklar tilbud Kr. 54.500,-!NaNDKK3626.00
419746200025050035000SEK 35.000,-ClassicNaNGullholmensnipa 21Used boatNaNVolvoPenta MD 20021 x 18 HP / 13 kWNaNNordsee » Västra Frölunda131NaNAnchor,Battery,Bilge Pump,Bilge pump,Cockpit Cover,Compass,Depth Instrument,Gas Stove,Stern Thruster,Teak Cockpit,Teak Deck,Underwater PaintSEK3185.00
520193108400003399EUR 3.399,-Fishing BoatLinder power boats355 Sportsmannew boat from stockAluminiumNaNNaNNaNGermany » Bayern » München58NaNNaNEUR3670.92
604107500003650CHF 3.650,-Sport BoatLinder power boatsFishing 410 (Aluminiumboot)new boat from stockAluminium(Permission for Lake of Constance)NaNNaNSwitzerland » Lake Constance » Uttwil132NaNNaNCHF4124.50
71999620350004003600CHF 3.600,-CatamaranNaNStoll SA YverdonUsed boat,UnleadedAluminiumYamaha F8 BMH 5.6 kWNaNUnleadedSwitzerland » Neuenburgersee » Yvonand474NaNAnchor,Mooring Cover,Swim Ladder,TrailerCHF4068.00
8030075000024800DKK 24.800,-Sport BoatNaNCrazy OneUsed boatNaN1 x 15 HP / 11 kW1 x 15 HP / 11 kWNaNDenmark » Svendborg134Crazy One´s elegante og spændende design kombinerer facinerende "motorcykel-egenskaber" med et dybt og tørt cockpit til 2 personer.,,Linieføring og udformning under vandlinien går Crazy One sjov og let at sejle, giver udfordring og spænding til race, vandski, fiskeri og som ledsagerfartøj - Den ideelle fritidsbåd for hele familien.,,Den lave vægt sikrer et sparsomt brændstofsforbrug og let håndtering på biltag, påhængsvogn, bådtrailer eller bag på en større motorbåd. Båden er udstyret med 15 hk 4-takts motor årgang 2010, sejlet minimalt af timer.NaNDKK3472.00
920193107700003333EUR 3.333,-Fishing BoatCrescent power boats364 Rodd 2.5 Packnew boat from stockNaNSuzuki DF 2.51 x 2 HP / 1.5 kWNaNGermany » Bayern » Boote+service Oberbayern45NaNFull EnclosureEUR3599.64
Year BuiltLengthWidthDepthDisplacementNumber of CabinsNumber of bedsFuel CapacityEngine HoursPriceCleanPriceBoat TypeManufacturerModelTypeMaterialEngineEngine PerformanceFuel TypeLocationNumber of views last 7 daysCommentsEquipmentCurrencyAmount_in_USD
10334041022000004990CHF 4.990,-Sport BoatPioner power boats14 Activenew boat on orderNaNNaNNaNNaNSwitzerland » Safenwil280NaNNaNCHF5638.70
10335041012500004980CHF 4.980,-Sport BoatLinder power boatsSportsman 400 (Aluminiumboot)new boat from stockAluminium(Permission for Lake of Constance)NaNNaNSwitzerland » Lake Constance » Uttwil247NaNNaNCHF5627.40
1033603108500004950CHF 4.950,-Sport BoatMarine power boats400 U - Alubootnew boat from stockAluminium(Permission for Lake of Constance)NaNNaNSwitzerland » Lake of Zurich » Stäfa ZH150NaNNaNCHF5593.50
103371984610000004950CHF 4.950,-Fishing BoatStaempfli power boats622Used boat,UnleadedPlasticHonda BF8D41 x 8 HP / 5.9 kWUnleadedSwitzerland » Bielersee » Gerolfingen288NaNFull Enclosure,TrailerCHF5593.50
103381987620000004900CHF 4.900,-Sport BoatSea Ray power boatsMonaco 200Used boat,UnleadedNaNOMC 5741 x 264 HP / 194 kWUnleadedSwitzerland » Lago Maggiore » Riazzino1'116NaNNaNCHF5537.00
10339041018500004516EUR 4.516,-Sport BoatNaNFOX BOATS 420C ECnew boat from stockGRPNaNNaNNaNGermany » Hamburg » HAMBURG94Länge: 4,17m | Breite: 1,67m | max. Motorisierung: 22 kW (30 PS) | Bordwandhöhe: 0,6m | zul. Ges.-Gewicht: 515 kg | Leergewicht: 185 kg | max. Zuladung: 300/385 kg | max. Personenzahl: 4/5 | Kategorie: C/D | Aufkimmung: 11°,Boot in Grundausstattung,Der Herrsteller Fox Boats hat sich zum Ziel gesetzt, durch Kooperation von Bootsbauern und erfolgreichen Sportfischern Boote zu bauen, welche genau auf die Bedürfnisse der Sportfischer zugeschnitten sind. Das neueste Produkt dieses Entwicklungskonzepts ist das Fox 420C. An seinem komfortablen Steuerstand, den vielen Staumöglichkeiten und durchdachten Extras wie dem integrierten Köderfischbecken ist die durchdachte Orientierung an den Anforderungen der Angler zu erkennen. Das geringe Leergewicht von nur 185 kg ermöglicht es, das Boot auch mit kleinen Zugfahrzeugen komfortabel auf einem Trailer zu ziehen.,Inkl . Ausstattung plus Transport Kosten,* Trailer auf Anfrage,,--- Zwischenverkauf und Irrtümer vorbehalten ---NaNEUR4877.28
1034020204100001204499EUR 4.499,-Sport BoatBlueCraft power boatsBlueSloep 140new boat from stock,UnleadedGRP(Permission for Lake of Constance)NaNUnleadedGermany » Nordrhein-Westfalen » Wesel354NaNNaNEUR4858.92
10341201841034000004300EUR 4.300,-Pontoon BoatWhaly power boats450 New Classicnew boat from stockNaNNaNNaNNaNItaly » Dormelletto266Caratteristiche tecniche,,Materiale: Polietilene Rotazionale,Motorizzazione: Gambo lungo,Lunghezza: 4,37 mt.,Larghezza: 1,89 mt,Altezza: 1,05 mt.,Altezza specchio di poppa: 0,67 mt.,Peso: 340 kg.,Capacità (persone): CE Cat. D 8 pers. - Cat C 4 pers,Peso massimo: CE Cat. D 800 kg. - Cat C 550 kg. (compreso motore),Potenza massima: 2 Kw elettrico - 6 CV (4,4 kw) - CE 8 cv (5,9 kw),Colorazioni: Rosso, Grigio, Giallo, Blu, Arancio, Nero, Grigio scuro, Verde e Marrone,Accessori: Timoneria,,Le imbarcazioni Whaly sono realizzate interamente con doppia parete plastica (PE) in unico pezzo e prodotte mediante la tecnica dello stampaggio rotazionale.,La progettazione, il metodo di produzione e la plastica polietilene di elevata qualità costituiscono le basi per imbarcazioni ultraresistenti, inaffondabili, indistruttibili e con necessità di manutenzione minima, caratterizzate da grande spazio interno, sicurezza e stabilità.,,Tutti i modelli Whaly 210, 270, 310, 370, 435 e 450 Classic si prestano ottimamente al settore ricreativo, mentre i modelli 310, 370 e 435 sono multifunzionali e sono perfetti a fini professionali. Nei modelli Whaly ad uso professionale sono fondamentalii requisiti, spesso estremi, posti dal cliente: una scuola di vela, ad esempio, desidera un’imbarcazione guida stabile, sicura e resistente e nell’ambito della navigazione professionale le imbarcazioni Whaly sono utilizzate come mezzi di salvataggio a bordo, con certificazione 94/25/EG, 2003/44/EG e NEN-EN 1914 (opzionale).,,Nei modelli ad uso ricreativo, l’accento sempre posto sulla navigazione sicura e stabile, ma un occhio riservato anche allo styling, alla comodità d’uso e al comfort senza tralasciare sicurezza, affidabilità e durata.,,Grazie a simili caratteristiche, le imbarcazioni Whaly trovano diversi campi d’applicazione:,,Navigazione da diporto,Imbarcazione da lavoro,Natante di salvataggio,Pesca sportiva,Noleggio,Scuole nautiche,,Accessori optional: Consolle, remi, rollbar, tendalino, cuscineria e telo copertura totale.NaNEUR4644.00
1034219924102700030103500EUR 3.500,-Sport BoatFletcher power boatsBravoUsed boat,UnleadedNaNMariner 40 hp1 x 40 HP / 29 kWUnleadedHungary » EbesNaNTrailer comes with two new tyres and new wheel bearings, detachable trailer lights also included. I used this boat in UK and Croatia without any problems. Engine comes with two locks so that no one can steal it. I took it out yesterday 23rd July 2020 from my garage and started it with no problem at all. If any questions please do not hesitate to contact me and the price is negotiable.Battery,Fire Extinguisher,Full Enclosure,Speed Instrument,TrailerEUR3780.00
103432019310000003780CHF 3.780,-Fishing BoatDarekCo power boatsTexas 360new boat from stockGRPNaNNaNNaNSwitzerland » Brienzersee » Brienz194NaNNaNCHF4271.40

Duplicate rows

Most frequently occurring

Year BuiltLengthWidthDepthDisplacementNumber of CabinsNumber of bedsFuel CapacityEngine HoursPriceCleanPriceBoat TypeManufacturerModelTypeMaterialEngineEngine PerformanceFuel TypeLocationNumber of views last 7 daysCommentsEquipmentCurrencyAmount_in_USD# duplicates
019846200005004000EUR 4.000,-LaunchNaNZaccagnino ANACONDAUsed boatGRPBuck1 x 20 HP / 15 kWNaNItaly » Toscana » ToscanaNaNNaNNaNEUR4320.02